CATH-FunVar - Predicting Viral and Human Variants Affecting COVID-19 Susceptibility and Severity and Repurposing Therapeutics

CATH-FunVar - 预测影响 COVID-19 易感性和严重程度的病毒和人类变异并重新调整治疗用途

基本信息

批准号：
BB/W003368/1
负责人：
Christine Orengo
金额：
$ 14.89万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FW003368%2F1
关键词：
CATH FunVar Predicting Viral Human

项目摘要

SARS-CoV-2 has caused a pandemic resulting in millions of deaths worldwide and significant social and economic disruption. Although vaccine trials have been encouraging vaccines must be distributed globally and therapeutic interventions will be needed for some time. It is clear that some human populations are much more vulnerable to the disease. For example older men and black and Asian communities. The factors causing these differences are still unclear and whilst social, economic and cultural issues are likely to be important, genetic factors could also play a role. Furthermore, the biological mechanisms by which severe responses arise and increase morbidity are still not known.In this project we will analyse genetic variations (causing reside mutations in the proteins) in diverse human populations (e.g. gender, ethnicity, people with severe responses) and in SARS-CoV-2. We will use structural and evolutionary data to determine whether the mutations could affect binding between the virus and human proteins. Human proteins in which mutations do affect binding will be mapped to protein networks to identify biological pathways that could be affected. We have powerful tools for functionally annotating proteins and the pathway modules in which they operate. Our data will rationalise the impacts on disease severity and improve diagnostics for populations at risk. Finally, proteins in these pathways are likely to be effective drug targets and we will use our protein family data to identify or repurpose suitable drugs having low side effects. We will also analyse related coronaviruses to identify future risks.We have already established a website (https://funvar.cathdb.info/uniprot/dataset/covid) providing mapping of SARS-CoV-2 viral proteins, functional annotations and proximity of mutations to known/predicted functional sites. This is currently populated with preliminary pilot data. It will be extended to host interactors and provide information on pathways and repurposed drugs.Research PlanWe will: (a) Classify 'human interactor' proteins interacting with viral proteins into CATH-FunFams to extract known or predicted structures and map variants (residue mutations) from different genders and populations onto these structures.(b) Perform FunVar analyses to identify mutations in human interactor and SARS-CoV-2 proteins likely to have functional impacts.(c) Map human interactors to a protein network to highlight biological processes implicated in host response and differentially affected between different genders/ethnicities(d) Identify human interactors which have clinically approved drugs or which map to FunFams from which clinically approved drugs can be repurposed.(e) Disseminate information via FunVar-COVID19 pages Our pipeline will detect diverse variants in different human populations, likely to be impacting functions and affecting Covid-19 response. It will also analyse available drug data to suggest possible therapeutics. Furthermore, our pipeline will be generic and will also be used to analyse other closely related coronavirus genomes that could pose future risks.

SARS-CoV-2 引发了一场大流行，导致全球数百万人死亡，并造成严重的社会和经济混乱。尽管疫苗试验令人鼓舞，但疫苗必须在全球范围内分发，并且在一段时间内仍需要治疗干预措施。显然，某些人群更容易感染这种疾病。例如老年男性以及黑人和亚洲社区。造成这些差异的因素仍不清楚，虽然社会、经济和文化问题可能很重要，但遗传因素也可能发挥作用。此外，引起严重反应和增加发病率的生物学机制仍不清楚。在这个项目中，我们将分析不同人群（例如性别、种族、有严重反应的人）的遗传变异（导致蛋白质中的突变）和在 SARS-CoV-2 中。我们将利用结构和进化数据来确定突变是否会影响病毒与人类蛋白质之间的结合。突变确实影响结合的人类蛋白质将被映射到蛋白质网络，以识别可能受到影响的生物途径。我们拥有强大的工具来对蛋白质及其运作的途径模块进行功能注释。我们的数据将合理化对疾病严重程度的影响，并改善对高危人群的诊断。最后，这些途径中的蛋白质可能是有效的药物靶点，我们将使用我们的蛋白质家族数据来识别或重新利用具有低副作用的合适药物。我们还将分析相关冠状病毒，以确定未来的风险。我们已经建立了一个网站（https://funvar.cathdb.info/uniprot/dataset/covid），提供 SARS-CoV-2 病毒蛋白的图谱、功能注释和相似性已知/预测的功能位点的突变。目前已填充初步试点数据。它将扩展到宿主相互作用蛋白，并提供有关途径和重新利用药物的信息。研究计划我们将：（a）将与病毒蛋白相互作用的“人类相互作用蛋白”分类到 CATH-FunFam 中，以提取已知或预测的结构并映射变体（残留突变） (b) 进行 FunVar 分析，以确定人类相互作用物和 SARS-CoV-2 蛋白中可能产生功能影响的突变。(c) 将人类相互作用物映射到蛋白质网络强调与宿主反应有关的生物过程，以及不同性别/种族之间受到不同影响的生物过程(d) 识别拥有临床批准药物或映射到 FunFams 的人类互动者，从中可以重新利用临床批准的药物。(e) 通过 FunVar-COVID19 传播信息我们的管道将检测不同人群中的不同变异，这些变异可能会影响功能并影响 Covid-19 反应。它还将分析可用的药物数据以建议可能的治疗方法。此外，我们的管道将是通用的，还将用于分析其他密切相关的冠状病毒基因组，这些基因组可能会带来未来的风险。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Structural and energetic analyses of SARS-CoV-2 N-terminal domain characterise sugar binding pockets and suggest putative impacts of variants on COVID-19 transmission.

对 SARS-CoV-2 N 末端结构域的结构和能量分析表征了糖结合袋的特征，并提出了变异体对 COVID-19 传播的假定影响。

DOI：
http://dx.10.1016/j.csbj.2022.11.004
发表时间：
2022
期刊：
Computational and structural biotechnology journal
影响因子：
0
作者：
Lam SD
通讯作者：
Lam SD

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

预测影响 COVID-19 易感性的人类和病毒蛋白变异并重新调整治疗用途

DOI：
10.1038/s41598-024-61541-1
发表时间：
2023-11-08
期刊：
Scientific Reports
影响因子：
4.6
作者：
V. Waman;P. Ashford;Su Datt Lam;Neeladri Sen;M. Abbasian;Laurel Woodridge;Yonathan Goldtzvik;N. Bor
通讯作者：
N. Bor

Insertions in the SARS-CoV-2 Spike N-Terminal Domain May Aid COVID-19 Transmission

SARS-CoV-2 刺突 N 端结构域的插入可能有助于 COVID-19 的传播